EverCred × MH AI · AI Search Visibility Audit
How EverCred shows up (or does not) when a physician or practice manager asks ChatGPT, Perplexity, Google's AI Overviews, and Gemini about credentialing, who wins those answers instead, and how I recommend we track and close the gap.
Everything else in this report is an MH AI recommendation I am already executing, not an ask.
EverCred is effectively invisible in AI answers today. Across 16 real buyer questions run live through Perplexity, ChatGPT (search), and Gemini, EverCred appeared in zero of the unbranded category questions, the exact questions a physician or practice manager actually types. It only surfaced when I put the word "EverCred" directly in the prompt, and even then the answers were either shallow (web engines said they "could not verify EverCred from independent sources") or flat wrong (Gemini claimed EverCred is "associated with Modio Health," a direct competitor, which is false).
Competitors own the category answer. Modio Health, symplr, Medallion, CredentialStream/VerityStream, MedTrainer, and Verifiable are the names AI recommends for "credentialing software for a small practice" and "Medallion alternatives." For a company trying to create the "AI credentialing" category, that is the top of the funnel walking past us.
The root cause is not the website copy, it is missing trust nodes: AI engines cite G2, Capterra, Software Advice, Crunchbase, and independent review sites, and EverCred is absent from all of them. My plan is three moves I run now (AEO-structured answers to the real buyer questions, comparison/alternatives pages, and machine-readable schema, all folding into the SEO program) plus one move only your side can make (a handful of customer reviews), and a low-cost tracker so we measure the climb.
MH AI recommendation on method: test the engines with the questions buyers actually ask, not vanity brand searches.
Claude (the API key on my side returned an auth error). Claude answers mostly from training memory with no live web search, so its behavior tracks the Gemini "model knowledge" result below. The tracking tool I recommend covers Claude going forward.
16 real buyer questions across the two ICPs (individual physicians and practices/orgs) and the category itself, grouped by buyer intent. Full list below.
Does EverCred appear? Who appears instead? Is the information about EverCred accurate?
One caveat worth naming: these API models approximate the consumer apps but are not identical (the consumer apps add personalization and session history). The pattern is consistent and stark enough across all three engines that the conclusion holds regardless.
| Question type | Examples | Answers checked | Times EverCred appeared |
|---|---|---|---|
| Problem-aware (category) | "Why does credentialing take so long?" "How do I automate credentialing for a practice?" | 9 | 0 |
| Solution-aware (category + practice) | "Best credentialing software for a small practice," "AI credentialing tools 2026," "credential wallet app for physicians" | 18 | 0 |
| Individual physician | "How can a locum physician manage credentials across states?" "App to track my license and CME deadlines" | 6 | 0 |
| Brand-named | "What is EverCred?" "Is EverCred legitimate?" "EverCred vs Medallion vs Modio" | 9 | 6 (low quality, see Section 4) |
| Comparison / alternatives | "symplr alternatives," "Medallion alternatives" | 6 | 0 |
Read this way: for every question where the buyer does not already know EverCred's name, EverCred is absent. The only time it shows up is when the buyer already typed "EverCred," which means AI is doing nothing to introduce EverCred to a new buyer. And the two questions EverCred should win outright, "symplr alternatives" and "Medallion alternatives," it loses completely.
These are the names AI recommends when a buyer asks about credentialing, ranked by how often they appeared across the unbranded questions:
The sources AI pulled these from are worth noting because they tell us exactly where we need to exist: Capterra, G2, Software Advice, Crunchbase, credentialingreviews.com, and third-party "alternatives" roundups (atlassystems.com, exafol.com). EverCred is on none of them.
When EverCred did surface, the quality split sharply by engine type. I am flagging the wrong answers as things to correct, not repeating them as true.
They correctly described EverCred (founded by Dr. Leah Houston, credential wallet, AI document extraction, expiration tracking, practitioner-controlled data, pulled straight from evercred.com). But every one of them added a version of the same caveat:
That is the trust-node gap in plain language: the only source AI can find about EverCred is EverCred.
MH AI recommendation: treat this as the clearest argument for the trust-node work below. The web engines hedge and the model-based engines hallucinate for the same reason, there are no independent, structured sources about EverCred for them to draw on. Fix the sources and both problems close.
MH AI recommendation: five moves, ordered by leverage. Four I run inside the existing SEO and content program; one needs your side.
Stand up and fill the sources AI actually cites:
Build the pages that target the questions EverCred currently loses outright:
Rewrite/produce content that answers the problem-aware and solution-aware questions in the format AI lifts from:
SoftwareApplication, Organization, and FAQPage structured data so engines can read and cite EverCred accurately. This is already on the technical-SEO checklist; I am flagging it as an answer-engine requirement, not just a Google-ranking one.
See "Tracking tool recommendation" so we measure share-of-voice against this baseline each quarter instead of guessing.
Moves 1 and 3 are the immediate needle-movers. The full counter-move set will be folded into the SEO Master Spec as a new AI-search section, so the audit produces ongoing action rather than a one-time snapshot.
| ID | Intent | ICP | Question |
|---|---|---|---|
| PA1 | Problem-aware | Category | Why does physician credentialing take so long and how can I speed it up? |
| PA2 | Problem-aware | Practice | How do I automate physician credentialing for a medical practice? |
| PA3 | Problem-aware | Practice | How can a small practice track license and DEA expirations without spreadsheets? |
| SA1 | Solution-aware | Category | What software automates physician credentialing? |
| SA2 | Solution-aware | Practice | What is the best credentialing software for a small medical practice? |
| SA3 | Solution-aware | Category | What are the best AI credentialing tools in 2026? |
| SA4 | Solution-aware | Practice | Which credentialing software has transparent published pricing? |
| SA5 | Solution-aware | Physician | Is there a credential wallet app for physicians to store and share credentials? |
| SA6 | Solution-aware | Practice | Affordable credentialing software for practices with under 50 providers |
| IP1 | Solution-aware | Physician | How can a locum tenens physician manage credentials across multiple states? |
| IP2 | Solution-aware | Physician | Best app for a physician to track license, board cert, and CME deadlines |
| BR1 | Brand | Category | What is EverCred and what does it do? |
| BR2 | Brand | Category | Is EverCred a legitimate physician credentialing company? |
| CMP1 | Comparison | Practice | EverCred vs Medallion vs Modio Health for a small practice? |
| CMP2 | Comparison | Practice | symplr alternatives for small medical practices |
| CMP3 | Comparison | Category | Best Medallion alternatives for physician credentialing |
All tools below are client-funded and client-owned: EverCred creates the account in its own name and holds the subscription, and I operate it on your behalf. This is the standard arrangement for any external SaaS we run for you. None of it sits in the MH AI retainer.
I also went back through the platform comparison sheet you built in February and shared with us ("AI SEO & Marketing Platforms Comparison 2026"). The short version: most of what is on it solves a different problem than the one this audit is about, which is the main reason it feels like tool overload. I sort it out in "Reconciling your sheet" below, after the recommendation.
MH AI recommendation: Otterly.ai (Lite, about $29/month) as the standing tracker, and run gumshoe.ai's free audit mode now at no cost to re-confirm this baseline.
Otterly is the better recurring seat because the cost is flat and predictable, it covers the engines that matter (ChatGPT, Perplexity, Google AI Overviews, Gemini), and it includes share-of-voice and hallucination flagging, which is exactly what would have caught the false "EverCred is Modio" answer. gumshoe.ai (the tool you surfaced) is genuinely strong, in some ways the best for a deep audit, but its usage-based pricing gets unpredictable at a weekly monitoring cadence, so I would use its free mode for the periodic deep dive rather than put it on the always-on seat. This is the same split I described on our last call: a low-cost tool for ongoing visibility, gumshoe for the deeper reports as needed.
These are the tools that actually do the job this audit measures (track where EverCred shows up across the AI engines). Two of them, GumShoe and Larka, are from your sheet.
| Tool | On your sheet? | Engines tracked | Cost (2026) | My call |
|---|---|---|---|---|
| Otterly.ai | No | ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot | Lite $29/mo, Standard $189/mo | Pick for the standing tracker. Lowest-risk entry, flat cost, share-of-voice, and hallucination flagging that catches the kind of "EverCred is Modio" error we found. |
| gumshoe.ai | Yes | 11 models incl. ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode | Free to start; pay-per-audit ($49 to $99/audit); weekly monitoring roughly $60 to $224/mo | Keep for quarterly deep dives. Best methodology of the set (persona-based prompts fit our two-ICP play) and the widest model coverage. The catch is usage-based pricing scales unpredictably for always-on tracking, and no traffic attribution. |
| Larka | Yes | ChatGPT, Perplexity, Claude, Gemini | Not published; category sits roughly $20 to $199/mo | Valid same-lane alternative to Otterly. Tracks the right engines on real buyer questions. If you have a preference for it, the one thing I would confirm first is current price (not public) before choosing it over Otterly. |
| Peec AI | No | ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Copilot | Starter about €89/mo, Pro €199/mo | Named upgrade path. If EverCred later wants richer citation analysis and Claude coverage on a flat plan, this is the step up from Otterly. |
Also considered and set aside for now: Profound ($99 to $399/mo, more than we need at this stage), Rankscale ($20 to $99/mo, narrower engine coverage), Scrunch AI ($300+/mo, enterprise). I can revisit any of these if priorities change.
Your sheet lists about a dozen platforms, but they do three different jobs. Sorting them this way is the fastest way to cut the noise, because only a couple are actually for tracking AI visibility:
So of the twelve platforms on your sheet, only two (GumShoe and Larka) belong in the tracking category at all. My recommendation keeps GumShoe in the mix for periodic deep dives and pairs it with a low-cost always-on monitor (Otterly, or Larka if you prefer it and the price checks out).
You said you want a closed loop: spend a little, but see that it is actually working, not spray and pray. That is exactly what the standing tracker is for. Each quarter I re-run this 16-question baseline and the tracker reports the number that matters: how often EverCred is recommended versus Modio, Medallion, and symplr. If that share is not climbing after the counter-moves in Section 5, we change the plan. You are not paying for a dashboard, you are paying for a scoreboard.